Appendix for: Multivariate Multiscale Entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences

1 Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100 Torun, Poland 2 Faculty of Physics, University of Warsaw, Pasteur 5 Street, 02-093 Warsaw, Poland 3 Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Grudziądzka 5 Street, 87-100 Torun, Poland.

Table A1.Descriptive statistics for the AUC (area under curve), MaxSlope, AvgEnt and the DiffEnt features of the multivariate Multiscale Entropy (mMSE) vector determined for the channel sets corresponding to the seven resting-state networks (Yeo et al., 2011) for females (A.) and males (B.), separately.DMN-default mode network, DAN-dorsal attention network, FPN-frontoparietal network, LN-limbic network, SMN-somatomotor network, VAN-ventral

Effect of channel sets
For AUC, the highest values of complexity were obtained for the following sets: F, C, P, MR, ML, and the lowest for FL, FR, PL, PR.For MaxSlope, the highest for F and ML, and MR; the lowest for PL and PR were revealed.For AvgEnt, the highest values of complexity were obtained for sets F, C, P, MR, ML, and the lowest for FL, FR, PL, PR.

Effect of s/g
Generally, in women, there was greater brain signal complexity at fine-grained scales (MaxSlope) than in men (p < 0.01).In men, there was greater entropy at coarse-grained timescales (AvgEnt) (p < 0.01).

Interaction effects of channel set x s/g
Taking into account the interaction effect (s/g x channel set: F(8,87)= 1.936, p = 0.064, η² p = 0.151, tendency level), it should be emphasized that the s/g differences were observed at fine-scales (MaxSlope) for the following regions: F, FL, FR, on tendency level.In women, there was greater brain signal complexity at fine-grained scales for F, FL, and FR regions than in males.At coarse-grained timescales (AvgEnt) interaction effect (s/g x channel set: F(8,87)= 1.649, p = 0.123, η² p = 0.132) wasn't significant.For AUC, the interaction effect was significant F(8,87)= 2.088, p = 0.045, η² p = 0.161), but only PL (the lower level of complexity) differed significantly from other regions.To sum up, there was the greatest general brain signal complexity (AUC) for the P set, and the lowest complexity was observed for FL, FR, Pl, and PR sets.No significant differences in AUC were revealed for F, C, ML, and MR sets.For MaxSlope the greatest level of complexity were observed for F, FL, P, and ML, MR channel sets.For AvgEnt the greatest level of complexity was observed for C and P sets, the lowest for FL, FR, PL, PR.
For AUC, the highest values of complexity were obtained for the following sets: F, C, P, MR, ML, and the lowest for FL, FR, PL, PR.For MaxSlope, the highest for F and ML, and MR; the lowest for PL and PR were revealed.For AvgEnt, the highest values of complexity were obtained for sets F, C, P, MR, ML, and the lowest for FL, FR, PL, PR.
Both the previous (Dreszer et al., 2020) and the current study showed s/g differences in MaxSlope (F > M) and AvgEnt (F < M, in the previous study -on tendency level).Differences between men and women for AUC were shown only in the previous study (Dreszer et al., 2020), this effect was not confirmed in the re-analysis taking into account the nine sets of electrodes in the current study.
The differences between men and women at fine scales were statistically significant mainly for the channels from the frontal sets: F, FL, FR (the effect was demonstrated in both studies).The previous study (Dreszer et al., 2020) also showed a difference in the MR and P sets, which were not shown in the current study.

Supplementary analysis A3.
We repeated the analysis taking into account three segments of mMSE features.
The choice of the number of segments was strongly dependent on the number of participants in whom these segments were identified.The 5-min rsEEG data acquisition block was divided into 40-sec.
segments resulting in 10240 samples (the signal was down-sampled to 256 Hz).We chose the first three uncut segments of the signal and treated "segment" as a within-subject factor in mixed ANOVA.

Fig. A. 6 .
Fig. A.6.The dynamics of MSE changes (DiffEnt: the difference between #9 and #4 timescales) for particular resting-state networks across the timescales and three segments (bars marked with different textures: segment #1 (stripes), segment #2 (checkered), segment #3 (dotted)).DMN-default mode network, DAN-dorsal attention network, FPN-frontoparietal network, LN-limbic network, SMN-somatomotor network, VAN-ventral attention network, and VN-visual network.DiffEnt values for the limbic network were significantly higher compared to other networks.On the other hand, both DAN and VN exhibited significantly lower values.

Fig. A. 7 .
Fig. A.7.The s/g-related differences in the dynamics of MSE changes (DiffEnt: the difference between #9 and #4 timescales) for particular resting-state networks across the timescales.DMN-default mode network, DAN-dorsal attention network, FPN-frontoparietal network, LN-limbic network, SMN-somatomotor network, VAN-ventral attention network, VN-visual network.Males -bars marked with beige; females -bars marked with checkered texture.
attention network, VN-visual network.